Emanuel Viebahn

Modern generative AI systems have shown the capacity to produce remarkably fluent language, prompting debates both about their semantic understanding and, less prominently, about whether they can perform speech acts. This paper addresses the latter question, focusing on assertion. We argue that to be capable of assertion, an entity must meet two requirements: it must produce outputs with descriptive functions, and it must be capable of being sanctioned by agents with which it interacts. The second requirement arises from the nature of assertion as a norm-governed social practice. Pre-trained large language models that have not been subject to fine-tuning fail to meet the first requirement. Language models that have been fine-tuned for “groundedness” or “correctness” may meet the first requirement, but fail the second. We also consider the significance of the point that AI systems can be used to generate proxy assertions on behalf of human agents.

The standard Kratzerian analysis of modal auxiliaries, such as ‘may’ and ‘can’, takes them to be univocal and context-sensitive. Our first aim is to argue for an alternative view, on which such expressions are polysemous. Our second aim is to thereby shed light on the distinction between semantic context-sensitivity and polysemy. To achieve these aims, we examine the mechanisms of polysemy and context-sensitivity and provide criteria with which they can be held apart. We apply the criteria to modal auxiliaries and show that the default hypothesis should be that they are polysemous, and not merely context-sensitive. We then respond to arguments against modal ambiguity. Finally, we show why modal polysemy has significant philosophical implications.